This document contains information about digital image processing. It discusses digital and image processing concepts like analog to digital conversion, image resolution, color models, basic operations, arithmetic operations, and morphological transformations. It provides examples of image processing techniques like thresholding, blurring, erosion, dilation, and contour detection. It also lists applications of image processing in various domains such as photography, medical imaging, character recognition, and remote sensing.

1. ROLL NO:11610125
NAME:RAHUL CHAHEL
SECTION:C8
SEMESTER:4TH
D.I.P
11610125 1

2.  DIGITAL:Digital describes electronic technology that
generates, stores, and processes data in terms of two
states: positive and non-positive. Positive is expressed or
represented by the number 1 and non-positive by the
number 0.
 IMAGE PROCESSING:The analysis and manipulation of
A digitized image
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3.  ADC
 IMAGE RESOLUTION
 COLOR MODELS
 BASIC OPERATIONS
 ARTHMETIC OPERATIONS
 IMAGE PROCESSING
 REFRENCES
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4. Analog-to-digital converter is a system that
converts an analog signal, such as a sound
picked up by a microphone or light entering
a digital camera, into a digital signal. An ADC
may also provide an isolated measurement
such as an electronic device that converts an
input analog voltage or current to a digital
number representing the magnitude of the
voltage or current. Typically the digital
output is a two’s complement binary number
that is proportional to the input, but there
are other possibilities.
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5. Resolution refers to the number of pixels in
an image. Resolution is sometimes identified
by the width and height of the image as well
as the total number of pixels in the image
 Pixels- Smallest controllable element of a picture
 Size of Image-No. Of pixels*Color depth
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6.  RGB-Red,green,blue
 CMYK-Cyan, Magenta, Yellow, And Key (Black)
 HSV-Hue, Saturation, Value
 HSL-Hue, Saturation, Lightness etc..
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7.  Access pixel values and modify them
 Access image properties
 Setting Region of Image (ROI)
 Splitting and Merging images(The B,G,R channels of an
image can be split into their individual planes when needed. )
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8.  IMAGE ADDITION
 IMAGE BLENDING(Weighted addition)
 BITWISE OPERATION(AND,OR,NOR,XOR)
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9.  CHANGING COLORSPACES
 GEOMETRIC TRANSFORMATION OF IMAGES
 IMAGE THRESHOLDING
 SMOOTHING IMAGE
 MORPHOLOGICAL TRANSFORMATION
 CONTOURS
and many more.....
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10. D.I.P 11610125 10

11.  There are more than 150 color-space conversion
methods available.Example:RGB2GRAY
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12. Performing vaiours geometric operations
like:Rotation,shifing,moving etc
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90 degree rotation
180 degree rotation

13. If the pixel value is greater than a threshold
value, it is assigned one value (may be
white), else it is assigned another value (may be
black).
Different types are:
 BINARY
 BINARY_INV
 TRUNC
 TOZERO
 TOZERO_INV
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14. Image afterThresholding
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15. As for one-dimensional signals, images also
can be filtered with various low-pass filters
(LPF), high-pass filters (HPF),etc. A LPF helps
in removing noise, or blurring the image.
Types:Averaging,Gaussian Blur,Median,
Bilateral Filtering
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16. D.I.P 11610125 16
Image after Blur

17. Morphological transformations are some simple
operations based on the image shape. It is
normally performed on binary images. It needs
two inputs, one is our original image, second one
is called structuring element or kernel which
decides the nature of operation. Two basic
morphological operators are Erosion and
Dilation. Then its variant
forms like Opening, Closing, Gradient etc also
comes into play. We will see them one-by-one
with help of following
image
Example:Erosion ,Dilation ,Opening ,Closing
,Morphological Gradient
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18.  Contours can be explained simply as a curve
joining all the continuous points (along the
boundary), having same color or intensity.
The contours are a useful tool for shape
analysis and object detection and
recognition.
 For better accuracy, use binary images. So
before finding contours, apply threshold or
canny edge detection.
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19.  Computerized photography (e.g., Photoshop)
 Space image processing (e.g., Hubble space
telescope images, interplanetary probe images)
 Medical/Biological image processing (e.g.,
interpretation of X-ray images, blood/cellular
microscope images)
 Automatic character recognition (zip code,
license plate recognition)
 Finger print/face/iris recognition
 Remote sensing: aerial and satellite image
interpretations
 Reconnaissance
 Industrial applications (e.g., product
inspection/sorting)
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20.  OpenCV-Python Tutorials
Documentation by Alexander Mordvintsev
& Abid K :https://docs.opencv.org/3.0-
beta/doc/py_tutorials/py_tutorials.html
 https://en.wikipedia.org/wiki
 http://www.emrclub.in/pages/projectView.p
hp
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